Francis J. M. Schmitt

Multispectral color image capture using a liquid crystal tunable filter

We describe the experimental setup of a multispectral color image acquisition system consisting of a professional monochrome CCD camera and a tunable filter in which the spectral transmittance can be controlled electronically. We perform a spectral characterization of the acquisition system taking into account the acquisition noise. To convert the camera output signals to device-independent color data, two main approaches are proposed and evaluated. One consists in applying regression methods to convert from the K camera outputs to a device-independent color space such as CIEXYZ or CIELAB. Another method is based on a spectral model of the acquisition system. By inverting the model using a principal eigenvector approach, we estimate the spectral reflectance of each pixel of the imaged surface.

Silhouette and stereo fusion for 3D object modeling

We present a new approach to high quality 3D object reconstruction. Starting from a calibrated sequence of color images, the algorithm is able to reconstruct both the 3D geometry and the texture. The core of the method is based on a deformable model, which defines the framework where texture and silhouette information can be fused. This is achieved by defining two external forces based on the images: a texture driven force and a silhouette driven force. The texture force is computed in two steps: a multistereo correlation voting approach and a gradient vector flow diffusion. Due to the high resolution of the voting approach, a multigrid version of the gradient vector flow has been developed. Concerning the silhouette force, a new formulation of the silhouette constraint is derived. It provides a robust way to integrate the silhouettes in the evolution algorithm. As a consequence, we are able to recover the apparent contours of the model at the end of the iteration process. Finally, a texture map is computed from the original images for the reconstructed 3D model.

The augmented multiresolution reeb graph approach for content-based retrieval of 3d shapes

This article presents a 3D shape matching method for 3D mesh models applied to content-based search in database of 3D objects. The approach is based on the multiresolution Reeb graph (MRG) proposed by Hilaga et al.1 MRG provides a rich representation of shapes able in particular to embed the object topology. In our framework, we consider 3D mesh models of various geometrical complexity, of different resolution, and when available with color texture map. The original approach, mainly based on the 3D object topology, is not accurate enough to obtain satisfying matching. Therefore we propose to reinforce the topological consistency conditions of the matching and to merge within the graph geometrical and visual information to improve matching and calculation of shape similarity between models. Besides, all these new attributes can be freely weighted to fit the user requirements for object retrieval. We obtain a flexible multiresolutional and multicriteria representation that we called augmented multiresolution Reeb graph (aMRG). The approach has been tested and compared with other methods. It reveals very performant for the retrieval and the classification of similar 3D shapes.

An adaptive subdivision method for surface-fitting from sampled data

A method is developed for surface-fitting from sampled data. Surface-fitting is the process of constructing a compact representation to model the surface of an object based on a fairly large number of given data points. In our case, the data is obtained from a real object using an automatic three-dimensional digitizing system. The method is based on an adaptive subdivision approach, a technique previously used for the design and display of free-form curved surface objects. Our approach begins with a rough approximating surface and progressively refines it in successive steps in regions where the data is poorly approximated. The method has been implemented using a parametric piecewise bicubic Bernstein-Bézier surface possessing G1 geometric continuity. An advantage of this approach is that the refinement is essentially local reducing the computational requirements which permits the processing of large databases. Furthermore, the method is simple in concept, yet realizes efficient data compression. Some experimental results are given which show that the representation constructed by this method is faithful to the original database.

Fast global registration of 3D sampled surfaces using a multi-z-buffer technique

We present a new method for the global registration of several overlapping three-dimensional (3D) surfaces sampled on an object. The method is based on the ICP algorithm and on a segmentation of the unstructured sampled points in an optimized set of z-buffers. This multi-z-buffer technique provides a 3D space partitioning which allows the registration process to detect quickly all the overlapping surfaces and to concentrate on them even when the surfaces overlap each other only slightly. It also greatly accelerates the search of the nearest neighbours in the establishment of the point-to-point correspondence between two overlapping surfaces. Then a randomized iterative registration is processed on the surface set. We have tested an implementation of this technique on real sampled surfaces. It appears to be rapid, accurate and robust, especially in the case of highly curved objects.

Intrinsic Surface Properties from Surface Triangulation

Intrinsic surface properties are those properties which are not affected by the choice of the coordinate system, the position of the viewer relative to the surface, and the particular parameterization of the surface. In [2], Besl and Jain have argued the importance of the surface curvatures as such intrinsic properties for describing the surface. But such intrinsic properties may be useful only when they can be stably computed. Most of the techniques proposed so far for computing surface curvatures can only be applied to range data represented in image form (see [5] and references therein). But in practice, it is not always possible to represent the sampled data under this form, as in the case of closed surfaces. So other representations must be used. Surface triangulation refers to a computational structure imposed on the set of 3D points sampled from a surface to make explicit the proximity relationships between these points [1]. Such structure has been used to solve many problems [1]. One question concerning such structure is what properties of the underlying surface can be computed from it. It is obvious that some geometric properties, such as area, volume, axes of inertia, surface normals at the vertices, can be easily estimated [1]. But it is less clear how to compute some other intrinsic surface properties. In [8], a method for computing the minimal (geodesic) distance on a triangulated surface has been proposed. Lin and Perry [6] have discussed the use of surface triangulation to compute the Gaussian curvature and the genus of surface. In this paper, we propose a scheme for computing the principal curvatures at the vertices of a triangulated surface.

3D Model Retrieval Using Probability Density-Based Shape Descriptors

We address content-based retrieval of complete 3D object models by a probabilistic generative description of local shape properties. The proposed shape description framework characterizes a 3D object with sampled multivariate probability density functions of its local surface features. This density-based descriptor can be efficiently computed via kernel density estimation (KDE) coupled with fast Gauss transform. The non-parametric KDE technique allows reliable characterization of a diverse set of shapes and yields descriptors which remain relatively insensitive to small shape perturbations and mesh resolution. Density-based characterization also induces a permutation property which can be used to guarantee invariance at the shape matching stage. As proven by extensive retrieval experiments on several 3D databases, our framework provides state-of-the-art discrimination over a broad and heterogeneous set of shape categories.

Augmented Reeb graphs for content-based retrieval of 3D mesh models

This work presents an improved method of 3D mesh models indexing for content-based retrieval in database with shape similarity and appearance queries. The approach is based on the multiresolutional Reeb graph matching presented by Hilaga et al. (2001). The original method only takes into account topological information what is often not sufficient for effective matchings. Therefore we proposed to augment this graph with geometrical attributes. We also provide a new topological coherence condition to improve the graph matching. Moreover 2D appearance attributes and 3D features are extracted and merged to improve the estimation of the similarity between models. Besides, all these new attributes are user-dependent as they can be weighted by variable terms. We obtain a flexible multiresolutional and multicriteria representation called augmented Reeb graph (ARG). Good preliminary results have been obtained in a shape-based matching framework compared to existing methods based only on statistical measures. In addition, our study lead us to an innovative part matching scheme based on the same approach as our augmented Reeb graph matching.

A Solution for the Registration of Multiple 3D Point Sets Using Unit Quaternions

Registering 3D point sets is a common problem in computer vision. The case of two point sets has been analytically well solved by several authors. In this paper we present an analytic solution for solving the problem of a simultaneous registration of M point sets, M>2, by rigid motions. The solution is based on the use of unit quaternions for the representation of the rotations.

Silhouette Coherence for Camera Calibration under Circular Motion

We present a new approach to camera calibration as a part of a complete and practical system to recover digital copies of sculpture from uncalibrated image sequences taken under turntable motion. In this paper, we introduce the concept of the silhouette coherence of a set of silhouettes generated by a 3D object. We show how the maximization of the silhouette coherence can be exploited to recover the camera poses and focal length. Silhouette coherence can be considered as a generalization of the well-known epipolar tangency constraint for calculating motion from silhouettes or outlines alone. Further, silhouette coherence exploits all the geometric information encoded in the silhouette (not just at epipolar tangency points) and can be used in many practical situations where point correspondences or outer epipolar tangents are unavailable. We present an algorithm for exploiting silhouette coherence to efficiently and reliably estimate camera motion. We use this algorithm to reconstruct very high quality 3D models from uncalibrated circular motion sequences, even when epipolar tangency points are not available or the silhouettes are truncated. The algorithm has been integrated into a practical system and has been tested on more than 50 uncalibrated sequences to produce high quality photo-realistic models. Three illustrative examples are included in this paper. The algorithm is also evaluated quantitatively by comparing it to a state-of-the-art system that exploits only epipolar tangents